论文标题
使用高斯流程偏好学习预测推文的幽默性
Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning
论文作者
论文摘要
迄今为止，大多数幽默处理系统最多可以在漫画和传统之间进行离散的粗粒差异，但是这样的概念可以更好地概念化为广泛的范围。在本文中，我们提出了一种概率方法，即高斯流程偏好学习（GPPL）的一种变体，该方法通过利用人类偏好判断并自动提供语言注释来学会对短文的幽默进行排名和评估。我们应用了我们的系统，该系统类似于以前在带有成对幽默注释注释的英语单线上表现出良好性能的系统，而Haha@Iberlef2019评估活动的西班牙语数据集。我们报告了广告系列的两个子任务的系统性能，即幽默检测和有趣的得分预测，并讨论了一些问题是由于HAHA@iberlef2019数据中使用的数字得分之间的转换以及我们方法所需的成对判断注释。
Most humour processing systems to date make at best discrete, coarse-grained distinctions between the comical and the conventional, yet such notions are better conceptualized as a broad spectrum. In this paper, we present a probabilistic approach, a variant of Gaussian process preference learning (GPPL), that learns to rank and rate the humorousness of short texts by exploiting human preference judgments and automatically sourced linguistic annotations. We apply our system, which is similar to one that had previously shown good performance on English-language one-liners annotated with pairwise humorousness annotations, to the Spanish-language data set of the HAHA@IberLEF2019 evaluation campaign. We report system performance for the campaign's two subtasks, humour detection and funniness score prediction, and discuss some issues arising from the conversion between the numeric scores used in the HAHA@IberLEF2019 data and the pairwise judgment annotations required for our method.
